A New 3D Segmentation Algorithm Based on 3D PCNN for Lung CT Slices

Author

Chang, Qian ; Shi, Jun ; Xiao, Zhiheng

Author_Institution

Sch. of Commun. & Inf. Eng., Shanghai Univ., Shanghai, China

fYear

2009

fDate

17-19 Oct. 2009

Firstpage

1

Lastpage

5

Abstract

Three-dimension (3D) based image data analysis has an important role for significantly improving the detection and diagnosis of lung disease with computed tomography (CT). In this paper, we proposed a new volume-based 3D segmentation algorithm based on the extended 3D pulse coupled neural network (PCNN) model. This algorithm was successfully used to segment the lung field in CT slice with the mean distance, root means square distance and Tanimoto coefficient of 0.0029plusmn0.0005, 0.0715plusmn0.0056, 0.9760plusmn0.0093, respectively. Furthermore, the means running time was only 273s, which was much less than those of 2D PCNN segmentation algorithm and Otsu algorithm. The experimental results demonstrated the extended 3D PCNN segmentation algorithm had the advantage of short execution time with good segmentation accuracy. The results suggest that the proposed 3D PCNN algorithm can be potentially used for lung computer-aided diagnosis.

Keywords

computerised tomography; diseases; image segmentation; lung; medical image processing; neural nets; 3D PCNN; 3D image data analysis; 3D pulse coupled neural network; 3D segmentation algorithm; Tanimoto coefficient; computed tomography; computer aided diagnosis; lung CT slices; lung disease detection; lung disease diagnosis; Cancer detection; Clustering algorithms; Computed tomography; Data analysis; Deformable models; Image segmentation; Lungs; Neural networks; Neurons; Pulse generation;

fLanguage

English

Publisher

ieee

Conference_Titel

Biomedical Engineering and Informatics, 2009. BMEI '09. 2nd International Conference on

Conference_Location

Tianjin

Print_ISBN

978-1-4244-4132-7

Electronic_ISBN

978-1-4244-4134-1

Type

conf

DOI

10.1109/BMEI.2009.5305554

Filename

5305554